Access to Transportation: Bus Network and Spatial Inequality in Columbus

Eungang (Peter) Choi

10/22/2020

Reading

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Seoul, Korea

  • Korean War (1950)
  • Massive growth, urbanization
  • Population: 10M (20% of S.Korea’s population)
  • Wider metropolitan area: 25M (48% of S.Korea’s population)

Short Video on Seoul’s growth and Public Transportation

Public Transportation in Seoul

  • 2004 BRT (Bus Rapid Transit) reform
    • bus route redesigns and bus lane expansions
    • frequent network of priority bus lines
    • seamless, free transfers between bus and subway

Result

  • Bus Ridership increased by 14% (2004), and 20% (2010)
  • Bus speeds doubled
  • Faster travel times for cars

More Space for People

  • overpaths and elevated freeways gone
  • more walk paths and nature

Cheonggyecheon

  • 3 mile stretch of elevated freeway built over a stream
  • restored in 2005, uncovering nature
  • less air polution
  • lower temperature in nearby areas during summer
  • “car-oriented city to a human-oriented city”
  • number of fish species 4 -> 25
  • bird species 6 -> 36
  • insect species 15 -> 192

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Seoul-ro

  • elevated highway near Seoul Station
  • built in 1970 to accomodate growing traffic congestion in Seoul (1970)
  • Project 7017
    • turn a highway into a pedestrian walkway
    • people-friendly city
    • boost in local economy (connectivity and access to small shops)

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Columbus, OH

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Research Questions

What is the relationship between the bus network and spatial inequality in Columbus?

  • Income levels
  • Low access (food deserts)
  • Race

Data & Methods

Bus Stop Data

Source: Smart Columbus Data

COTA Bus stop data (Sept. 2018)

  • geocodes
  • bus lines passing through the stop

Create Network

  1. Arrange the stops by each lines
  2. Connect the stops (nodes) in each lines (forming ties)
  3. Calculate Centrality

Centrality

  • Measuring the importance of a node
  • Having the most ties to other actors in the network
  • More access to other parts of the city

Food Access Research Atlas Data

Source: USDA

  • tract-level data on food access (supermarket accessibility)
  • Low income
  • low vehicle access
  • by various demographic groups (race, age)

Key Variables

  • Median Family Income: tract median family income (standardized)
  • Low Access Vehicles: more than 100 households without vehicles & beyond 1/2 mile from supermarket
  • Low Access Population(%): Share of Low Access(1/2 mile) population in tract
  • Low Access Race/Ethnicity(%): Share of Low Access(1/2 mile) in tract population by race/ethnicity
  • WILL BE ADDING URBAN/RURAL VARIABLE

Results

  • WILL BE REPLACED WITH GRAPHS

Results

  • WILL BE MAKING CHANGES

Conclusion

  • Access to transportation is not equally provided to the people that need it most
  • Accordance with the article (“Long Commutes Are Awful, Especially for the Poor”)